System and method for optimizing financial performance generated by marketing investments under budget constraints

ABSTRACT

Methods, systems, and computer program products are provided for optimizing financial performance. Time series data describing the financial performance generated by corresponding marketing investments is provided to configure an econometric model. Linear coefficients of the econometric model are adjusted in accordance with the qualitative factors received as inputs from experts. The econometric model is transformed into an aggregated non-linear econometric model that includes non-linear factors that cause the financial performance to change at a varying rate as a function of the marketing investments. An allocation of the marketing investments generated by the aggregated non-linear econometric model is adjusted by applying an optimization algorithm to provide an optimized financial performance.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention relating to both structure and method ofoperation may best be understood by referring to the followingdescription and accompanying drawings:

FIG. 1A illustrates an embodiment of a framework for quantifyingfinancial impact of marketing investments;

FIG. 1B illustrates, in a tabular form, an embodiment of time seriesdata described with reference to FIG. 1A;

FIG. 1C shows a block diagram of an embodiment of a method to adjustintercept (Alpha) and slope (Beta) coefficients of the one or morelinear econometric models described with reference to FIG. 1A;

FIG. 1D shows a block diagram of an embodiment of a method to transformlinear econometric models to non-linear econometric models describedwith reference to FIG. 1A;

FIG. 1E shows a block diagram of an embodiment of a method to performoptimization on an aggregated non-linear econometric model describedwith reference to FIG. 1A;

FIG. 2A is a histogram generated by an embodiment of an optimizationmodule to illustrate an optimized return on investment corresponding toeach marketing vehicle;

FIG. 2B is a histogram generated by embodiment of an optimization moduleto illustrate an optimized return on investment corresponding to eachbusiness unit;

FIG. 2C is a summary in tabular form provided to a decision maker byembodiment of an optimization module to illustrate changes in investmentcompared to current investment;

FIG. 3 is a flow chart of an embodiment of a method for optimizingfinancial performance; and

FIG. 4 illustrates a block diagram of an embodiment of a computersystem.

DETAILED DESCRIPTION

Novel features believed characteristic of the present disclosure are setforth in the appended claims. The disclosure itself, however, as well asa preferred mode of use, various objectives and advantages thereof, willbest be understood by reference to the following detailed description ofan illustrative embodiment when read in conjunction with theaccompanying drawings. The functionality of various modules, devices orcomponents described herein may be implemented as hardware (includingdiscrete components, integrated circuits and systems-on-a-chip ‘SoC’),firmware (including application specific integrated circuits andprogrammable chips) and/or software or a combination thereof, dependingon the application requirements. The accompanying drawings may not to bedrawn to scale and some features of embodiments shown and describedherein may be simplified or exaggerated for illustrating the principles,features, and advantages of the disclosure.

The following terminology may be useful in understanding the presentdisclosure. It is to be understood that the terminology described hereinis for the purpose of description and should not be regarded aslimiting.

Framework—A basic infrastructure designed to provide one or morefunctions. A framework used in an information technology (IT)environment may typically include electronic hardware, software, andservices building blocks that are designed to work with each other todeliver core functions and extensible functions. The core functions aretypically a portion of the framework that may not be modifiable by theframework user. The extensible functions are typically a portion of theframework that has been explicitly designed to be customized andextended by the framework user as a part of the implementation process.

Model—A model can be a representation of the characteristics andbehavior of a system, element, solution, product, or service. A model asdescribed herein captures the behavior of a marketplace that includessuppliers and consumers of products and services. The model may includea declarative specification of the structural, functional,non-functional, and runtime characteristics of the marketplace. Themodel may be often used to as a simulation tool to predict future valuesof the dependent variable based on the input values of the independentvariables and to play various what-if scenarios in business planning.

Marketing—As defined by the American Marketing Association, marketing isthe activity, set of institutions, and processes for creating,communicating, delivering, and exchanging offerings that have value forcustomers, clients, partners, and society at large.

Marketing investments—Allocation and expenditure of resources towardsone or more marketing activities that are directed to achieve desiredbusiness results.

Service—Utility or benefit provided by a provider to a consumer. Theprovider and the consumer may vary by application and may include anenterprise, a business unit, a business process, an application, a thirdparty, an individual, and similar others. Enterprise services may beprovided in the course of conducting the enterprise business.

System—One or more interdependent elements, components, modules, ordevices that co-operate to perform one or more predefined functions.

Configuration—Describes a set up of elements, components, modules,devices, and/or a system, and refers to a process for setting, defining,or selecting hardware and/or software properties, parameters, orattributes associated with the elements, components, modules, devices,and/or the system.

Expected financial results and the financial performance of investmentsmade by companies may be measured and benchmarked in terms of financialparameters such as revenues, profits, return on investment (ROI), returnon capital (ROC), payback, and similar others. The characteristics ofthe business-to-consumer (B2C) marketplace such as short sales cycle,relative simplicity of products, limited distribution channels (e.g.,retail or direct), well established consumer buying behavior and similarothers generally improves the availability of measurable data for agiven marketing investment. That is, the impact of marketing investmentssuch as advertising, product rebate campaigns and the like on therevenue of a particular consumer product sold to a consumer may bequantifiable.

In a business-to-business (B2B) marketplace the products and servicesare typically sold by one business to another business. While companiesparticipating in the B2B marketplace may have sophisticated programs todeliver messaging to their intended target audiences, they have not beeneffective in quantifying returns on marketing investments. Thecharacteristics of the B2B marketplace may include a diversity of highlycomplex or advanced technology products, a multiplicity of servicesassociated with the products, complex distribution channels, e.g.,products sold via a combination of direct and indirect channels, valueadded resellers, solution providers, original equipment manufacturers(OEMs) and similar others, adds to the challenge of measuring marketingeffectiveness. In addition, in the B2B space, factors such as longersales cycles and delayed effect of marketing investments on revenues mayfurther complicate the quantification of returns on marketinginvestments. In an era of shrinking marketing budgets with ever risingrevenue goals, measuring marketing effectiveness and modifyinginvestment strategies is a competitive advantage and a businessimperative.

Applicants recognize that it would be desirable to provide a frameworkfor the measurement and quantification of the financial impact ofmarketing investments to determine their effectiveness. That is, itwould be desired to provide a robust model operable in a framework thatis based on a standardized platform, the model being used to accuratelypredict financial performance given a portfolio of marketinginvestments. Applicants further recognize that it would be desirable forthe robust model to take into account non-linear factors affecting thebehavior of the B2B marketplace including delayed impact of marketinginvestments on revenues, diminishing impact of marketing investments onrevenues, seasonality of demand and similar others.

Systems and methods disclosed herein provide a framework (may also bereferred to as a generic architecture from which application specificarchitectures may be configured) for optimizing financial performance.The framework that is based on a standardized platform provides anon-linear econometric model for accurately predicting financialperformance given a portfolio of marketing investments and anoptimization module to optimize the financial performance. Time seriesdata describing the financial performance generated by correspondingmarketing investments is provided to configure an econometric model.Linear coefficients of the econometric model are adjusted in accordancewith the qualitative factors received as inputs from experts. Theeconometric model is transformed into an aggregated non-lineareconometric model that includes non-linear factors that cause thefinancial performance to change at a varying rate as a function of themarketing investments. An allocation of the marketing investmentsgenerated by the aggregated non-linear econometric model is adjusted byapplying an optimization algorithm to provide an optimized financialperformance.

Framework for Quantifying Financial Performance

FIG. 1A illustrates an exemplary framework 100 for optimizing financialperformance, according to an embodiment. The framework 100 includes aplurality of modules that co-operatively interact with one another, withexternal applications such as statistical application packages andspreadsheets, and with users to provide information describing theeffectiveness and the impact of the marketing investments on thefinancial performance. The plurality of modules includes time seriesdata 116, one or more linear econometric models 120, expert opinions 110used for Delphi adjustment 114 and Bayesian adjustment 112 of the one ormore linear econometric models 120, non-linear factors 130 to transformthe one or more linear econometric models 120 into an aggregatednon-linear econometric model 140, an optimization under constraint 150module to provide an optimal allocation of a given portfolio ofmarketing investments to optimize financial performance 160.Econometrics, as described herein, is the field of economics that isconcerned with the application of mathematical statistics and the toolsof statistical inference to the empirical measurement of relationshipspostulated by economic theory. Econometric models, which are oftendeveloped in accordance with historical empirical data, are deployed topredict values of dependent variables based the values of one or moreindependent variables.

The framework 100, which may be based on a standardized, commerciallyavailable computer platform, may be used as an easy to use, intuitivetool to plan marketing strategies, assess various marketing budgetallocation scenarios and select marketing investment strategies tomaximize financial performance including revenues and profits.Additional details of the standardized computer platform are describedwith reference to FIG. 4.

The framework 100 leverages non-linear modeling to determine marketingvehicles that significantly impact revenues and profits, the Bayesianand Delphi adjustments 112 and 114 to incorporate expert opinions 110 ina quantifiable manner, and mathematical optimization 150 for determiningan optimal mix of marketing investments 160 to maximize financialreturns. The framework 100 encapsulates the entire process to optimizethe marketing investments and provides an easy to use user interface forthe decision maker.

As described herein, a marketing vehicle (may also be referred to as amarketing tool or instrument) is simply a form of marketingcommunication that serves to reach a target audience. Examples ofmarketing vehicles may include brand advertising, promotionaladvertising, direct marketing, trade shows and seminars, sponsoredevents, web marketing and similar others. In an exemplary model tosimulate optimized allocation for maximizing the financial performanceof a B2B marketplace, marketing investments in nine marketing vehicleswere used including brand advertising, demand generation advertising,external communications, sales collaterals, direct marketing, events,field enablement, web marketing and non event sponsorships.

The framework 100 is operable to construct (may also be referred to asconfigure, define, or generate) one or more linear econometric models120 based on the time series data 116 as input. A linear econometricmodel 122 (also referred to as simply an econometric model) included inthe one or more linear econometric models 120 is represented by amathematical equation E100 that linearly relates a dependent variable(e.g., financial performance) and a set of independent variables (e.g.,multiple marketing vehicles). In each model a particular set ofindependent variables may be selected by conducting a statisticalanalysis to determine the most significant ones having the largestimpact on revenue.

Specifically, Equation E100 may be expressed in terms of a linearfunction as follows:

$\begin{matrix}{R = {{Alpha} + {\sum\limits_{i = 1}^{n}{{Beta}_{i}^{*}x_{i}}}}} & {{Equation}\mspace{14mu} {E100}}\end{matrix}$

where R (the dependent variable) is the estimated revenue (a measure offinancial performance) computed by the linear econometric model 122,Alpha is an intercept coefficient of the linear model and corresponds tothe estimated revenue without using any marketing vehicles (e.g., withno marketing effort, referred to as a base revenue), Beta_(i) is theslope of the linear model associated with the i^(th) marketing vehicleand corresponds to a change in the estimated revenue for each additionaldollar of investment in the i^(th) marketing vehicle, x_(i) (theindependent variable) is the actual investment in the i^(th) marketingvehicle, and Alpha, Beta₁ through Beta_(1-n) are constants and arereferred to as linear coefficients.

Since the one or more linear econometric models 120 may not capture thecomplexities and the behavior of the B2B marketplace the one or morelinear econometric models 120 are enhanced to include other qualitativeor subjective factors such as macroeconomics trends (e.g., projectionsof future IT spending, the effect of prime lending rates, geo-politicaldisturbances, epidemics, wars, global warming, price of oil, and similarothers) and expert opinions 110 that influence revenues. Additionaldetails of the impact of qualitative or subjective factors on the one ormore the linear econometric models 120 are described with reference toFIG. 1D.

The one or more linear econometric models 120 are also enhanced toinclude unique, non-linear characteristics of the B2B marketplacecompared to those of the B2C marketplace. Adstocking can be used totransfer the effect of an independent variable to future months therebydelaying impact of an investment in a marketing vehicle on revenue,e.g., caused due to a longer sales cycle. Adbudging can be used tocomprehend a smaller return on investment as the investments increase.The unique characteristics of the B2B marketplace are captured bytransforming the one or more linear econometric models 120 into multiplenon-linear models 142 and forming an aggregated non-linear econometricmodel 140 from the multiple non-linear models 142. Additional details ofthe transformation from the linear econometric models into non-linearones are described with reference to FIG. 1E.

An optimization module 150, which performs optimization under budgetconstraints, assesses marketing investments in various marketingvehicles and recommends an optimized set or portfolio of marketinginvestments 160 that maximizes the desired financial performance. Inaddition, the optimization module 150 provides a simple, easy to useinterface to the decision maker. Additional details of the optimizationmodule 150 are described with reference to FIG. 1F.

Predicted optimized revenues and corresponding allocations for marketinginvestments 160 computed by the optimization module 150 may be comparedwith the actual data by a refinement module 170 to further refine themodels 120 and 140 and the non-linear factors 130, thereby improving thefidelity and the accuracy (by reducing the error) of the predictionrelative to the one or more linear econometric models 120. Therefinement of the linear and non-linear econometric models may beimplemented by modifying business rules that may be used to implementthe various models.

Time Series Data

FIG. 1B illustrates in a tabular form time series data 116 describedwith reference to FIG. 1A, according to an embodiment. The time seriesdata 116 includes previously recorded actual values for the financialperformance, e.g., revenues, generated by investments made incorresponding marketing vehicles (MV1 through MVx) for a defined timeperiod, e.g., monthly, quarterly, annually, and similar other timeintervals. The time series data 116 may be recorded corresponding toeach business unit, geographic area, key account, product, or similarother classification. As described earlier, the framework 100 isoperable to construct one or more linear econometric models 120 based onthe time series data 116 used as an input. Regression analysis tools maybe used to determine the values for the Alpha (intercept) and Beta(slope) coefficients in the one or more linear econometric models 120 byinvoking the principle of least squares.

Delphi and Bayesian Adjustments

FIG. 1C is a block diagram showing an embodiment of a method 180 foradjusting intercept (Alpha) and slope (Beta) coefficients of the one ormore linear econometric models 120 described with reference to FIG. 1A.The Delphi adjustment 114 is a systematic and an interactive methodbased on independent inputs of selected experts. The method relies oncollective opinions gathered from a panel of carefully selected expertsfrom the marketing departments and business units within an enterprise.These experts answer a series of questions provided to them via severalwell designed survey questionnaires 108. A separate questionnaire may beprovided to the panel of experts for adjusting the intercept coefficient104 and the slope coefficient 106.

Questions may be formulated as hypotheses. The experts state theiropinion about the relevance of certain drivers of change with regard totheir business units and marketing vehicles on an ordinal scale, e.g.,on an 8 point scale. An exemplary list of questions may include: (1)Effect of HP pricing strategy in any of the product categories relativeto competition, (2) Sub-BU lifecycle (growth, maturity or decline) ofthe product category, (3) Introduction of new products in a businessarea by HP or by the competition, (4) Change in current and futuretrends in market demand for IT spending (5) Strengthening of sales forceby HP and streamlining sales processes (6) Change in trends in supplierpricing, (7) Reseller loyalty and a variety of other factors.

Each expert determines a quantitative impact of a qualitative orsubjective factor by assigning a numerical value in percentage terms.For example, one expert may determine that the effect of a product lifecycle is significant and may thus assign a high value on an eight-pointscale. Similarly other experts quantify the influence of the productlife cycle. Their stated inputs are consolidated and an average iscomputed as the expert opinions 110. In the event of wildly differingand dissenting assessments (measured by standard deviation), adiscussion is moderated by an experienced and knowledgeable interlocutorto achieve consensus. This approach is applied to every question in thequestionnaire and an overall average is computed. This average value isadded to the estimated intercept coefficient (Alpha obtained in EquationE100). If the intercept value is “a” and the estimated impact ofexternal factors is 4%, then “a” is updated to “1.04a”.

A commercially available web based survey delivery and management toolthat provides an easy to use interface for the survey design, deliveryand analysis of the responses to the questions may be used to adjust themodels and moderate a discussion amongst the experts effectively. Sincethe econometric model is based on periodic revenues and investments bymarketing vehicles each slope coefficient corresponds to the relativeeffect of a marketing vehicle on revenue.

A separate set of questions are presented to and answered by the expertsfor updating the slope (Beta) coefficients. The questions are based onthe premise that the raison d'etre of marketing is to deliver the rightmessage to the right audience at the right time. So the marketingquestionnaire may include questions related to (1) quality of content inthe messaging, (2) the ability to target a qualified audience, and, (3)deliver advertising at the appropriate time. The experts answer thesequestions relative to each one of the marketing vehicles deployed andeach slope coefficient 106 is updated according to the estimated averageimpact of the three drivers of marketing effectiveness. The updatedeconometric equation serves as the objective function to maximizerevenue. Towards this end, a mathematical optimization is invoked as thefinal step in determining the optimal mix of investments for incrementalrevenue generation.

The Bayesian adjustment 112 is used to incorporate qualitative orsubjective information about the probability distribution associatedwith the intercept and slope coefficients of the one or more lineareconometric models 120 to adjust the intercept and slope coefficients104 and 106 accordingly. Bayesian models use a probability model thataccurately describes the data known as the sampling distribution. TheBayesian models also use subjective information about the parameters ofthe sampling distribution/probability model. and can be viewed asimposing a probability measure. The sampling distribution and theprobability measures are combined to derive a posterior distribution.The time series data 116 describing the revenue and correspondingmarketing investments serves as the sampling distribution and the expertjudgment codified by a probability measure serves as the priordistribution.

Similar to the use of questionnaires to gather expert opinions 110 tomake Delphi adjustment 114, questionnaires may be used to gather expertopinions 100 to make the Bayesian adjustment 112. The consensus outputfrom the experts is used to codify the prior distribution, which is usedin the Bayesian modeling. Operationally, from the moderated enquiry andfeedback, a distribution of values is obtained for each questionanswered and is modeled by a probability distribution which serves as aprior distribution.

Linear to Non-Linear Model Transformation for the B2B Marketplace

FIG. 1D is a block diagram of a method 190 to transform lineareconometric models to non-linear econometric models described withreference to FIG. 1A, according to an embodiment. As described earlier,linear econometric modeling, when used in a B2B marketplace context, maybe subject to inaccuracies and errors since the linear econometricmodels may not account for non-linear characteristics of the B2Bmarketplace such as adstocking and adbudging. In marketing applications,it is desirable that the overall statistical models should comprehendmemory effects 132, diminishing returns 134, seasonality 136 and adiversity of other factors (not shown) to accurately predict financialperformance such as return on investments (ROI). Codifying thenon-linear behavior of B2B marketplace into the high fidelity model(shown as the aggregated non-linear econometric model 140) utilizes acombination of business acumen and mathematics. Equation E100representing the one or more linear econometric models 120 istransformed into multiple non-linear econometric models 142 inaccordance with Equation E200 as follows:

N(R _(i))=[delta*(R _(i))**tau]/[rho+(R _(i))**tau]  Equation E200

where R_(i) is the estimated revenue corresponding to the i^(th)marketing vehicle (per Equation E100), N(R_(i)) is the non-lineareconometric model, and delta, tau and rho are configurable parameters.The particular values for the parameters delta, tau and rho may beselected in the Equation E200 to represent the adbudging 136 effectsexperienced in a B2B marketplace.

Marketing vehicles exert influence on revenue over a long period knownas memory effect. It is simply the extent of the impact of a marketingvehicle on revenue carried over from one time period to subsequentperiods. Operationally memory effect is induced by adjusting the driversto produce exponentially decaying impact on revenue over time and isknown as adstocking in marketing parlance. The diminishing impact on ROIknown as adbudging postulates smaller return as investments increase.These non-linear effects are sewed into the overall model to capture allthe marketing dynamics of the B2B marketplace.

Adstocking is the fraction of a marketing vehicle's impact on revenueover future time periods. As the precise carry-over effect of a vehicleis unknown, adstocking may be defined as a fraction over a range ofvalues between (0, 1), with 0 corresponding to a marketing vehiclehaving an instantaneous effect of advertising on revenue, and 1corresponding to a marketing vehicle having a 100% future effect onrevenues in the future months. A value of adstocking between 0 and 1corresponds to x % of its effect being transferred to the future months,decaying gradually in an exponential fashion. Based on historical andsimulated data derived from experiments the range of values foradstocking is observed to be typically between (0.25, 45). Searching forthe best adstocking levels among all the nine marketing vehiclesdescribed may require examining over 100,000 models per business unit.

Seasonality 136 effects may be included by using techniques such asseasonally adjusted time series data and state space model based methodfor seasonal adjustment. The aggregated non-linear econometric model 140is an aggregation of the non-linear econometric models 142 to accuratelyquantify the statistical relationship between revenue and thecorresponding marketing vehicles.

Optimization

FIG. 1E is a block diagram of a method 195 to perform optimization onthe aggregated non-linear econometric model 140 described with referenceto FIG. 1A, according to an embodiment. The optimization module 150 isoperable to maximize revenue generated by marketing investments subjectto constraints on the overall marketing budget (business unit level,country level) and individual marketing investments expenditures(minimum and maximum level constraint per marketing investment).

The optimization module 150 uses a revenue equation estimated from thetime series data 116 as the objective function. The objective istypically a non-linear function (e.g., Equation E200) as defined by theaggregated non-linear econometric model 140. Using the overall plannedbudget and individual constraints on the marketing vehicles, a gradientsearch algorithm 152 is launched to find an optimal set of marketinginvestments to provide the highest yield in terms of desired financialperformance such as revenues and profits. Prior to optimization, anon-linear, profit (Y-axis) versus investment (X-axis), curve forproduct A shows an initial marketing investment 154 and a non-linearprofit/investment curve for product B shows an initial marketinginvestment 156. After optimization, the recommended investmentallocations for optimized financial performance 160, per computationsperformed by the optimization module 150, are shown to have a shift(increase) in marketing investment 156 to investment 162 and a shift(decrease) in marketing investment from investment 154 to investment164.

The platform 100 provides a built-in tool with a spreadsheet 168interface to implement the marketing portfolio optimization. This toolenables the user (typically a financial decision maker or marketingprofessional) to test various what-if marketing scenarios and determinethe best marketing investment strategy that provides the highest benefitin terms of desired revenues and profits.

The optimization algorithm is the gradient search algorithm 152 known asgeneralized reduced gradient (GRG) procedure. The GRG methods, which areselectable in many commercially available spreadsheet tools, arealgorithms for solving nonlinear programs of general structure. Theframework 100 provides a seamless conduit to incorporate the objectivefunction built or estimated in an external platform executingstatistical software 166 (including packages such as SAS, EXCEL, MATLAB,S-Plus, and similar others). The optimization module 150 provides autility to load the data and a custom interface that can performanalyses at various levels of granularity (country, business unit,product line levels).

There is a significant improvement in the prediction of the revenues fora given portfolio of marketing investments using the framework 100compared to the traditional linear model based predictions. In aparticular marketing investment application, the aggregated non-linearmodel 140 provided by the framework 100 results in an overall squaredcoefficient of correlation that is approximately equal to 80% and theaverage absolute percent error is approximately equal to 10%.

Optimized Return on Investment

FIG. 2A is a histogram 210 generated by the optimization module 150 toillustrate an optimized return on investment corresponding to eachmarketing vehicle, according to an embodiment. FIG. 2B is a histogram220 generated by the optimization module 150 to illustrate an optimizedreturn on investment corresponding to each business unit, according toan embodiment. FIG. 2C is a summary in tabular form 230 provided to adecision maker by the optimization module 150 to illustrate changes ininvestment compared to current investment, according to an embodiment.Referring to FIG. 2A, a sales collateral marketing vehicle 212 providesthe highest ROI of about 19% while an events marketing vehicle 214provides the lowest ROI of about 3.5%. Referring to FIG. 2B, a businessunit #3 222 provides the highest ROI of about 8.2% while a business unit#6 224 provides the lowest ROI of about 2%. Referring to FIG. 2C, theoptimized results indicate that investment in the sales collateralmarketing vehicle should be increased, e.g., by a factor of 2×, and theinvestment in event marketing should be decreased, e.g., by a factor of0.5×. Referring to FIGS. 2A, 2B, and 2C, the histograms 210 and 220 andsummary table 230 are indicative of the marketing effectiveness of theinvestments made in the corresponding marketing vehicles.

Method for Optimizing Financial Performance

FIG. 3 is a flow chart of a method 300 for optimizing financialperformance, according to an embodiment. In a particular embodiment, themethod can be used for predicting and optimizing financial performanceusing the framework 100 described with reference to FIG. 1A.

At process 310, time series data is received, the time series dataincluding data describing the financial performance generated bycorresponding marketing investments made as a function of time. Atprocess 320, an econometric model is configured from the time seriesdata, the econometric model including linear coefficients that define aquantitative linear relationship between the financial performance andthe corresponding marketing investments. The configuring of theoperation model includes defining the linear coefficients such as theintercept and slope coefficients of the linear econometric model. Atprocess 330, the linear coefficients of the econometric are adjusted inaccordance with the qualitative factors, thereby enabling thequalitative factors to be quantified into the econometric model. Atprocess 340, the econometric model is transformed into an aggregatednon-linear econometric model, the aggregated non-linear econometricmodel including non-linear factors that cause the financial performanceto change at a varying rate as a function of the marketing investments.At process 350, an allocation of the marketing investments generated bythe aggregated non-linear econometric model is adjusted by applying anoptimization algorithm included in the optimization module to provide anoptimized financial performance.

It is understood, that various steps described above may be added,omitted, combined, altered, or performed in different orders. Forexample, a process 322 may be added before process 330. At process 322,inputs are received from experts to quantify qualitative or subjectivefactors affecting the financial performance, the inputs being used tomodify an intercept and slopes associated with the econometric model.

Benefits of the tools and techniques for predicting financialperformance include a framework that uses a standardized computerplatform to provide easy to use interfaces for selecting the optimumportfolio of marketing investments. The framework 100 provides highfidelity models that accurately predict the non-linear behavior andcharacteristics of the B2B marketplace. In addition, qualitative andsubjective factors such as macroeconomic trends that have an effect onthe revenues are quantified and incorporated into the overall model.

Computer System

FIG. 4 illustrates a block diagram of a computer system 400, accordingto an embodiment that can be used to implement framework 100 and methods180, 190, 195, and 300. The computer system 400 includes a processor 410coupled to a memory 420. The memory 420 can be operable to store programinstructions 430 that are executable by the processor 410 to perform oneor more functions. It should be understood that the term “computersystem” can be intended to encompass any device having a processor thatcan be capable of executing program instructions from a memory medium.In a particular embodiment, the various functions, processes, methods,and operations described herein may be implemented using the computersystem 400. For example, the framework 100 or any components thereof,may be implemented using the computer system 400.

The various functions, processes, methods, and operations performed orexecuted by the system 400 can be implemented as the programinstructions 430 (also referred to as software or simply programs) thatare executable by the processor 410 and various types of computerprocessors, controllers, central processing units, microprocessors,digital signal processors, state machines, programmable logic arrays,and the like. In an exemplary, non-depicted embodiment, the computersystem 400 may be networked (using wired or wireless networks) withother computer systems.

In various embodiments the program instructions 430 may be implementedin various ways, including procedure-based techniques, component-basedtechniques, object-oriented techniques, rule-based techniques, amongothers. The program instructions 430 can be stored on the memory 420 orany computer-readable medium for use by or in connection with anycomputer-related system or method. A computer-readable medium can be anelectronic, magnetic, optical, or other physical device or means thatcan contain or store a computer program for use by or in connection witha computer-related system, method, process, or procedure. Programs canbe embodied in a computer-readable medium for use by or in connectionwith an instruction execution system, device, component, element, orapparatus, such as a system based on a computer or processor, or othersystem that can fetch instructions from an instruction memory or storageof any appropriate type. A computer-readable medium can be anystructure, device, component, product, or other means that can store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, or device.

The illustrative block diagrams and flow charts depict process steps orblocks that may represent modules, segments, or portions of code thatinclude one or more executable instructions for implementing specificlogical functions or steps in the process. Although the particularexamples illustrate specific process steps or acts, many alternativeimplementations are possible and commonly made by simple design choice.Acts and steps may be executed in different order from the specificdescription herein, based on considerations of function, purpose,conformance to standard, legacy structure, and the like.

While the present disclosure describes various embodiments, theseembodiments are to be understood as illustrative and do not limit theclaim scope. Many variations, modifications, additions and improvementsof the described embodiments are possible. For example, those havingordinary skill in the art will readily implement the steps necessary toprovide the structures and methods disclosed herein, and will understandthat the process parameters, materials, and dimensions are given by wayof example only. The parameters, materials, and dimensions can be variedto achieve the desired structure as well as modifications, which arewithin the scope of the claims. Variations and modifications of theembodiments disclosed herein may also be made while remaining within thescope of the following claims. For example, a few specific examples offinancial performance associated with corresponding marketing vehiclesare described. The illustrative framework for optimizing allocation ofthe marketing investments to maximize financial returns can be used withany suitable utility/investment models. That is, the illustrativetechniques may be used to maximize any utility delivered by an optimizedset of investments. For example, the illustrative techniques may be usedto improve total customer experience on a web site based on customerdependent web metrics and satisfaction ratings. In the claims, unlessotherwise indicated the article “a” is to refer to “one or more thanone”.

1. A method for optimizing financial performance, the method comprising:receiving time series data, the time series data including datadescribing the financial performance generated by correspondingmarketing investments made as a function of time; configuring aneconometric model from the time series data, the econometric modelincluding linear coefficients that define a quantitative linearrelationship between the financial performance and the correspondingmarketing investments; adjusting the linear coefficients in accordancewith qualitative factors, thereby enabling the qualitative factors to bequantified into the econometric model; transforming the econometricmodel into an aggregated non-linear econometric model, the aggregatednon-linear econometric model including non-linear factors that cause thefinancial performance to change at a varying rate as a function of themarketing investments; and adjusting an allocation of the marketinginvestments generated by the aggregated non-linear econometric model byapplying an optimization algorithm to provide an optimized financialperformance.
 2. The method according to claim 1 further comprising: theadjusting of the allocation of the marketing investments being subjectto an overall marketing budget constraint, a sum of the marketinginvestments not exceeding the overall marketing budget constraint. 3.The method according to claim 1 further comprising: the application ofthe optimization algorithm comprising applying a generalized reducedgradient (GRG) procedure, the GRG procedure being selectable within aspreadsheet tool.
 4. The method according to claim 1 further comprising:the non-linear factors comprising at least one of a memory effect, adiminishing return factor, and a seasonality factor.
 5. The methodaccording to claim 1 further comprising: the linear coefficientscomprising an intercept coefficient and a slope coefficientcorresponding to the marketing investments.
 6. The method according toclaim 5 further comprising: delivering via a web based delivery system aseparate set of questions corresponding to the intercept coefficient andthe slope coefficient; and receiving responses to the separate set ofquestions via the based delivery system to quantify the responses. 7.The method according to claim 1 further comprising: the financialperformance and the corresponding marketing investments being associatedwith business-to-business transactions.
 8. A computer system foroptimizing financial performance, the computer system comprising: acomputer processor; and logic instructions on computer readable mediaand executable by the computer processor to cause the computer processorto: receive time series data, the time series data including datadescribing the financial performance generated by correspondingmarketing investments made as a function of time; configure aneconometric model from the time series data, the econometric modelincluding linear coefficients that define a quantitative linearrelationship between the financial performance and the correspondingmarketing investments; adjust the linear coefficients in accordance withthe qualitative factors, thereby enabling the qualitative factors to bequantified into the econometric model; transform the econometric modelinto an aggregated non-linear econometric model, the aggregatednon-linear econometric model including non-linear factors that cause thefinancial performance to change at a varying rate as a function of themarketing investments; adjust an allocation of the marketing investmentsgenerated by the aggregated non-linear econometric model by applying anoptimization algorithm to provide an optimized financial performance. 9.The computer system according to claim 8 further comprising: logicinstructions to cause the computer processor to perform the adjusting ofthe allocation of the marketing investments that are subject to anoverall marketing budget constraint, a sum of the marketing investmentsnot exceeding the overall marketing budget constraint.
 10. The computersystem according to claim 8 further comprising: logic instructions tocause the computer processor to perform the application of theoptimization algorithm by applying a generalized reduced gradient (GRG)procedure, the GRG procedure being selectable within a spreadsheet tool.11. The computer system according to claim 8 further comprising: logicinstructions to cause the computer processor to define the non-linearfactors to include at least one of a memory effect, a diminishing returnfactor, and a seasonality factor.
 12. The computer system according toclaim 8 further comprising: logic instructions to cause the computerprocessor to provide the linear coefficients that comprise an interceptcoefficient and a slope coefficient corresponding to the marketinginvestments.
 13. The computer system according to claim 12 furthercomprising logic instructions to cause the computer processor to:deliver via a web based delivery system a separate set of questionscorresponding to the intercept coefficient and the slope coefficient;and receive responses to the separate set of questions via the baseddelivery system to quantify the responses.
 14. The computer systemaccording to claim 8 further comprising: logic instructions to cause thecomputer processor to provide the financial performance and thecorresponding marketing investments that are associated withbusiness-to-business transactions.
 15. A computer program product foroptimizing financial performance, the computer program productcomprising: logic instructions on a computer readable storage executableto cause a computer processor to: receive time series data, the timeseries data including data describing the financial performancegenerated by corresponding marketing investments made as a function oftime; configure an econometric model from the time series data, theeconometric model including linear coefficients that define aquantitative linear relationship between the financial performance andthe corresponding marketing investments; adjust the linear coefficientsin accordance with the qualitative factors, thereby enabling thequalitative factors to be quantified into the econometric model;transform the econometric model into an aggregated non-lineareconometric model, the aggregated non-linear econometric model includingnon-linear factors that cause the financial performance to change at avarying rate as a function of the marketing investments; adjust anallocation of the marketing investments generated by the aggregatednon-linear econometric model by applying an optimization algorithm toprovide an optimized financial performance.
 16. The computer programproduct of claim 15 further comprising: logic instructions on thecomputer readable storage executable to cause the computer processor toperform the adjusting of the allocation of the marketing investmentsthat are subject to an overall marketing budget constraint, a sum of themarketing investments not exceeding the overall marketing budgetconstraint.
 17. The computer program product of claim 15 furthercomprising: logic instructions on the computer readable storageexecutable to cause the computer processor to perform the application ofthe optimization algorithm by applying a generalized reduced gradient(GRG) procedure, the GRG procedure being selectable within a spreadsheettool.
 18. The computer program product of claim 15 further comprising:logic instructions on the computer readable storage executable to causethe computer processor to define the non-linear factors to include atleast one of a memory effect, a diminishing return factor, and aseasonality factor.
 19. The computer program product of claim 15 furthercomprising: logic instructions on the computer readable storageexecutable to cause the computer processor to provide the linearcoefficients that comprise an intercept coefficient and a slopecoefficient corresponding to the marketing investments.
 20. The computerprogram product of claim 19 further comprising logic instructions on thecomputer readable storage executable to cause the computer processor to:deliver via a web based delivery system a separate set of questionscorresponding to the intercept coefficient and the slope coefficient;and receive responses to the separate set of questions via the baseddelivery system to quantify the responses.